Gastos Casa
22 mayo, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 13/3/2023 | Comida | 51473 | Tami | NA |
| 13/3/2023 | Diosi | 20990 | Tami | Antiparasitario |
| 16/3/2023 | Vacunas Influenza | 19980 | Tami | NA |
| 20/3/2023 | Comida | 52314 | Tami | NA |
| 26/3/2023 | Comida | 24970 | Andrés | caramagnola |
| 28/3/2023 | Comida | 71805 | Tami | NA |
| 29/3/2023 | Electricidad | 42447 | Andrés | PAC ENEL 01686518 |
| 30/3/2023 | Netflix | 8320 | Tami | NA |
| 31/3/2023 | Comida | 13226 | Tami | NA |
| 31/3/2023 | Comida | 100000 | Andrés | wild foods |
| 31/3/2023 | Enceres | 15400 | Tami | Incoludido |
| 9/4/2023 | Gas | 67300 | Andrés | el de la derecha |
| 10/4/2023 | Comida | 61792 | Tami | NA |
| 17/4/2023 | Comida | 41602 | Tami | NA |
| 19/4/2023 | VTR | 21990 | Andrés | NA |
| 19/4/2023 | nacho | 55000 | Andrés | NA |
| 22/4/2023 | Comida | 19420 | Tami | NA |
| 23/4/2023 | Comida | 50617 | Tami | NA |
| 23/4/2023 | Crunchyroll | 49900 | Tami | NA |
| 23/4/2023 | Netflix | 5940 | Tami | NA |
| 28/4/2023 | Electricidad | 43471 | Andrés | NA |
| 29/4/2023 | Comida | 17000 | Andrés | pizza y dulces y nueces y almendras |
| 30/4/2023 | Comida | 84066 | Tami | NA |
| 30/4/2023 | Parafina | 38640 | Tami | NA |
| 7/5/2023 | Comida | 53654 | Tami | NA |
| 11/5/2023 | Cruz Verde | 21650 | Tami | Diolasa + propoleos |
| 14/5/2023 | Comida | 69636 | Tami | NA |
| 21/5/2023 | Comida | 71007 | Tami | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 6.3191e+08 2 6.567 0.0015 **
## lag_depvar 8.4157e+10 1 1749.167 <2e-16 ***
## Residuals 2.7761e+10 577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1015.411 13442.26 0.0177066
## 2-0 28309.564 22633.916 33985.21 0.0000000
## 2-1 21080.726 17700.670 24460.78 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
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## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
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## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
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## 28 28640.00 0 19668.29
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## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 425 50543.83 15394.107
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 1983.600504 4025.251707 -523.027938 2451.113310 -2939.883248
## 7 8 9 10 11
## 529.558800 -5639.950387 -1205.647252 -3985.832637 -456.431049
## 12 13 14 15 16
## -4972.678825 -1665.460572 -955.425514 326.526066 -3281.802748
## 17 18 19 20 21
## -428.674408 -2173.538984 6556.293092 -1527.170476 -1212.759774
## 22 23 24 25 26
## 1467.461531 -1181.428126 235.059873 1700.035215 -7083.955010
## 27 28 29 30 31
## 922.891065 8180.041749 461.337925 29.641541 -2359.183271
## 32 33 34 35 36
## 1600.919257 4607.184023 1188.806775 2455.863630 -1793.565244
## 37 38 39 40 41
## 4664.829400 4337.318051 -2219.226665 -2945.811731 -1097.108374
## 42 43 44 45 46
## -10736.619817 7227.593684 2548.493162 1375.227419 8121.193938
## 47 48 49 50 51
## 750.436814 6589.459847 6808.004845 -5758.815862 -4723.474913
## 52 53 54 55 56
## -5026.115009 -7930.098910 6079.869239 -4082.186082 -4924.069058
## 57 58 59 60 61
## 3799.895150 862.995843 -48.095136 128.317431 -5007.452659
## 62 63 64 65 66
## 18086.603868 3717.594307 -3556.126903 5981.845653 7430.297535
## 67 68 69 70 71
## 14759.944422 1889.701505 -13030.124241 -1227.558068 4704.546916
## 72 73 74 75 76
## -4817.945534 -4362.365291 -10487.292506 2411.689528 -5432.285504
## 77 78 79 80 81
## 1002.639920 -6912.699377 464.628196 -2422.694691 -2767.413517
## 82 83 84 85 86
## -4012.241378 -631.427239 2229.669875 3702.988062 448.077746
## 87 88 89 90 91
## -506.572232 174.636507 4284.187309 -1151.902616 1153.691911
## 92 93 94 95 96
## -2054.664459 -1048.102179 168.134215 267.891689 -7488.100808
## 97 98 99 100 101
## 2342.738966 -8630.331061 -3017.047948 -4123.721606 -1834.464771
## 102 103 104 105 106
## -1356.779242 3090.385938 -2401.350866 2528.269947 -1199.669247
## 107 108 109 110 111
## 927.663499 2555.949233 -3165.491494 -4751.701219 -903.784788
## 112 113 114 115 116
## 1851.929362 11660.435929 -1199.790255 2698.649560 4305.802649
## 117 118 119 120 121
## 3566.628482 -1022.281533 -4654.810337 -3698.766565 2319.546124
## 122 123 124 125 126
## -1718.284596 1342.776575 8868.872572 910.895837 191.744934
## 127 128 129 130 131
## -2466.532228 2687.861568 7097.776094 1095.455821 -8420.314440
## 132 133 134 135 136
## 1766.710070 4161.841497 -3115.338784 -1396.250416 -841.791667
## 137 138 139 140 141
## -3874.229758 1164.743579 -503.891460 -2923.639551 1691.938903
## 142 143 144 145 146
## -1893.165980 -7851.024649 1972.986062 -3525.025975 2041.673064
## 147 148 149 150 151
## -297.285674 986.926825 -384.343745 1328.375435 1174.334621
## 152 153 154 155 156
## 3353.338061 -4843.856836 -1188.205188 -3254.690683 5920.756176
## 157 158 159 160 161
## 9751.871351 -3350.337424 -4708.230643 3655.963865 281.878227
## 162 163 164 165 166
## 2793.069134 -5790.148542 -6658.568094 4213.113346 17485.932356
## 167 168 169 170 171
## 3825.682081 -186.995941 -2245.473512 -924.949526 3758.752275
## 172 173 174 175 176
## -42.788309 -7896.878580 2991.301457 4471.075786 795.054791
## 177 178 179 180 181
## 8919.678891 -9033.486751 -3318.953969 -10613.323014 -11168.960102
## 182 183 184 185 186
## 1250.781506 9331.836165 -1326.380519 6028.230726 6691.943581
## 187 188 189 190 191
## 13329.186540 8666.170637 -3799.004781 2685.316066 10586.755223
## 192 193 194 195 196
## -1385.274912 -2215.951606 -10081.752375 -6231.815648 1328.310653
## 197 198 199 200 201
## -5129.259791 -9718.173677 5414.903760 -2995.757556 -1649.510781
## 202 203 204 205 206
## -742.342113 6559.819086 9987.228208 733.820651 3076.151534
## 207 208 209 210 211
## 3258.380024 5953.584921 13024.487780 -5441.341641 -11097.715452
## 212 213 214 215 216
## -5537.671470 -10489.473409 -5028.969350 1557.123152 -12959.729498
## 217 218 219 220 221
## 16382.932202 7903.920965 1664.824813 26829.135458 12796.616753
## 222 223 224 225 226
## 7645.191885 14345.445288 -3553.530989 -1435.478264 4047.132939
## 227 228 229 230 231
## 627.529415 2995.155306 9250.589143 6107.170029 -1617.777432
## 232 233 234 235 236
## -1570.546922 9656.150099 -11244.013785 -7097.680873 -8402.066895
## 237 238 239 240 241
## -10009.313838 3121.228498 1421.821950 -8213.567156 -8943.483163
## 242 243 244 245 246
## 9104.979255 -7693.990769 2525.680574 -10239.243681 -4036.620947
## 247 248 249 250 251
## 1433.091643 1034.982918 -12267.614622 3638.619016 2092.101572
## 252 253 254 255 256
## 4262.840015 2214.749177 -1066.124187 11229.596432 21027.423732
## 257 258 259 260 261
## 3436.291843 -4037.433486 4303.158053 -1494.841962 3914.355373
## 262 263 264 265 266
## -4667.764567 -10745.041653 -4639.436419 -453.821098 -5118.534062
## 267 268 269 270 271
## 8826.816478 -4184.338594 4264.349621 -2010.470459 4516.278086
## 272 273 274 275 276
## 815.151358 7409.412719 -1275.655220 12147.041584 -4415.240136
## 277 278 279 280 281
## 1859.505287 -238.970317 7974.847633 -4906.230722 -2612.342477
## 282 283 284 285 286
## -11159.043879 -2616.955708 18701.653791 7915.823992 2891.662564
## 287 288 289 290 291
## -471.180927 1049.132593 6535.625046 7037.728787 -18599.908382
## 292 293 294 295 296
## -11054.766757 -8079.134678 9684.671224 3143.741118 -1090.557513
## 297 298 299 300 301
## 27487.675105 10260.016271 5118.899691 9735.733463 3092.165598
## 302 303 304 305 306
## -807.525149 8097.358187 -24080.147947 -3427.824808 -80.665360
## 307 308 309 310 311
## -6871.164911 -3897.527780 2999.116284 -9105.712332 -3170.857233
## 312 313 314 315 316
## -8127.116106 1607.061395 -3089.180347 2109.973009 -4003.019160
## 317 318 319 320 321
## 27517.427682 -566.825238 3436.066299 10979.109065 5767.606664
## 322 323 324 325 326
## 32566.036848 5398.504124 -20658.063920 1973.154419 1286.796927
## 327 328 329 330 331
## -6294.709096 -1594.804832 -33137.515312 933.440134 -2226.201845
## 332 333 334 335 336
## -7.061663 -3065.980789 4190.470696 -306.512325 -6815.177426
## 337 338 339 340 341
## -2993.959159 -2068.654224 -7553.041301 3963.845388 -1235.429545
## 342 343 344 345 346
## -1598.524116 -852.666194 321.837034 633.612648 -1460.435109
## 347 348 349 350 351
## -9290.333492 -13079.768442 2407.382665 -4205.275079 -3543.211604
## 352 353 354 355 356
## -5864.586573 1859.121697 1509.342265 2889.955091 -3616.856859
## 357 358 359 360 361
## -375.288685 820.876598 7162.475653 447.110773 131.072701
## 362 363 364 365 366
## 2750.473599 -2577.033079 -713.187389 -8580.886450 -4484.397698
## 367 368 369 370 371
## -6076.395805 -4821.851565 -7128.286491 5130.350305 509.302133
## 372 373 374 375 376
## 7264.678900 -7468.996014 -2113.895964 -3239.198107 -2321.163407
## 377 378 379 380 381
## -12311.356718 2023.479918 -10497.956928 5812.948997 9477.548112
## 382 383 384 385 386
## 3295.289630 -2222.837369 1772.222057 6914.450869 11594.544265
## 387 388 389 390 391
## -5598.802998 -5188.151472 -3.987842 8713.350306 1982.337107
## 392 393 394 395 396
## 11386.035796 -9696.017456 2919.082197 859.115412 705.204242
## 397 398 399 400 401
## -514.255341 -428.999437 -14357.142787 8624.184181 -1048.295271
## 402 403 404 405 406
## -1238.833845 7116.020180 -7779.585263 -1160.527946 -2391.860574
## 407 408 409 410 411
## -5678.301918 -2723.026965 -3777.984965 -8615.660046 6263.267441
## 412 413 414 415 416
## 1793.112114 -7212.663223 -7543.453865 14360.180559 3989.364475
## 417 418 419 420 421
## 4668.413596 -7855.741415 -4584.226362 -2447.816053 2973.074621
## 422 423 424 425 426
## -13847.000994 -2653.484084 -8956.927475 3145.073075 7125.860094
## 427 428 429 430 431
## 6743.884038 -3805.079793 -3947.846671 -4556.113082 -1629.829744
## 432 433 434 435 436
## -5550.907752 -6472.955348 -5803.556083 -1251.778883 -700.204149
## 437 438 439 440 441
## -4821.522294 2731.253298 5001.577211 -4880.379033 -1990.987642
## 442 443 444 445 446
## 1742.767697 -3663.587587 3003.375066 -6400.793698 -11945.564880
## 447 448 449 450 451
## -4368.550125 9787.512520 -1858.198208 4927.566852 -5681.632632
## 452 453 454 455 456
## -946.594955 561.222337 3208.097198 -12077.161988 3532.452985
## 457 458 459 460 461
## -6523.917573 6687.303845 3198.448492 2701.889059 -3645.193332
## 462 463 464 465 466
## 2283.167778 188.709707 1988.520276 -322.010842 3547.037239
## 467 468 469 470 471
## -2435.663408 6000.812496 -6732.690791 -2772.562882 -2017.990864
## 472 473 474 475 476
## -4477.994507 3175.147481 7988.062721 -5805.515928 1680.229766
## 477 478 479 480 481
## -5977.364118 -2656.663135 2196.672115 -12735.955334 -9591.456324
## 482 483 484 485 486
## -1055.930802 174.346616 -798.634835 -1172.958823 -9412.380618
## 487 488 489 490 491
## 11249.004079 6426.875078 7630.364918 -5208.669114 5577.456206
## 492 493 494 495 496
## 9515.628540 6296.135250 -13223.576439 -10357.953477 -3259.632719
## 497 498 499 500 501
## -927.776291 -343.108833 -7440.563231 782.235964 4468.442827
## 502 503 504 505 506
## 5706.568909 875.266944 296.898870 -7022.926416 767.148264
## 507 508 509 510 511
## -4846.323104 2023.943602 -1095.849839 -7957.913757 -420.741085
## 512 513 514 515 516
## -2488.566741 -403.501284 1520.531574 -9298.459429 -7590.485369
## 517 518 519 520 521
## 24447.371953 10059.338811 6134.949477 -5073.194848 3036.983361
## 522 523 524 525 526
## 17259.150677 11751.008924 -23855.854424 -4845.952049 -3534.518048
## 527 528 529 530 531
## 4762.084453 -152.809079 -10900.152225 4565.317927 14100.389270
## 532 533 534 535 536
## -4748.026157 4584.094726 5774.279285 -1561.011233 -4322.001567
## 537 538 539 540 541
## -6870.223287 -1914.866789 8506.552279 336.721213 -7932.509035
## 542 543 544 545 546
## 2003.337257 -403.868701 562.592320 -10831.461875 -10889.205881
## 547 548 549 550 551
## 2190.939944 7169.842534 -1124.951983 1029.582067 -7522.329185
## 552 553 554 555 556
## 8745.678478 1115.505827 -11732.653067 9342.149797 8866.089450
## 557 558 559 560 561
## 328.850628 5078.235689 -3340.995993 14326.620750 21749.445004
## 562 563 564 565 566
## -6171.174871 -9420.567383 6998.408127 461.600664 3683.092490
## 567 568 569 570 571
## -7146.036579 -17101.273496 6825.853975 6624.096056 2112.483735
## 572 573 574 575 576
## 3313.625883 1992.698915 -1939.585952 14934.327297 -9395.372541
## 577 578 579 580 581
## -6025.587732 8911.414376 3082.454340 -6317.181326 7716.274841
## 582
## -3572.600651
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17285.69 20113.75 24339.17 24059.03 26396.60 23747.16 24458.66 19722.79
## 10 11 12 13 14 15 16 17
## 19461.12 16821.72 17593.96 14345.32 14396.14 15056.33 16741.52 15072.82
## 18 19 20 21 22 23 24 25
## 16100.54 15478.28 22513.17 21603.33 21086.68 22964.00 22294.51 22942.68
## 26 27 28 29 30 31 32 33
## 24776.24 18745.39 20459.96 28244.66 28301.93 27977.04 25622.37 27015.39
## 34 35 36 37 38 39 40 41
## 30832.62 31178.71 32578.42 30105.74 34105.68 37292.23 34368.10 31200.39
## 42 43 44 45 46 47 48 49
## 30055.91 20698.69 28166.94 30587.06 31668.95 38461.13 37959.11 42590.00
## 50 51 52 53 54 55 56 57
## 46797.82 39544.76 34149.69 29205.81 22396.27 28644.04 25247.64 21570.10
## 58 59 60 61 62 63 64 65
## 25948.86 27199.95 27494.97 27904.02 23802.68 40282.55 42114.13 37392.01
## 66 67 68 69 70 71 72 73
## 41570.70 46453.34 57049.87 55076.98 40419.27 37941.88 40939.52 35277.94
## 74 75 76 77 78 79 80 81
## 30760.72 21526.60 24706.57 20659.65 22731.70 17661.51 19663.41 18895.13
## 82 83 84 85 86 87 88 89
## 17929.38 16011.28 17280.47 20864.30 25252.35 26235.57 26260.36 26872.96
## 90 91 92 93 94 95 96 97
## 30970.33 29808.74 30801.38 28878.82 28084.01 28449.68 28853.53 22474.12
## 98 99 100 101 102 103 104 105
## 25468.90 18546.19 17410.01 15463.89 15761.64 16434.47 20877.07 19966.73
## 106 107 108 109 110 111 112 113
## 23454.24 23245.62 24910.48 27767.92 25282.84 21750.21 22023.78 24652.28
## 114 115 116 117 118 119 120 121
## 35443.79 33648.78 35473.91 38452.09 40394.85 38098.81 32954.62 29320.60
## 122 123 124 125 126 127 128 129
## 31389.43 29680.94 30854.56 38403.25 38048.11 37115.96 34000.57 35769.80
## 130 131 132 133 134 135 136 137
## 41131.40 40575.46 31836.29 33092.59 36260.91 32695.68 31093.79 30184.94
## 138 139 140 141 142 143 144 145
## 26765.11 28170.03 27941.21 25643.06 27653.88 26287.88 19933.01 22943.17
## 146 147 148 149 150 151 152 153
## 20784.47 23741.57 24277.93 25857.63 26038.48 27681.52 28973.52 31985.29
## 154 155 156 157 158 159 160 161
## 27485.92 26753.83 24325.53 30179.99 41370.77 39712.23 37094.89 42081.41
## 162 163 164 165 166 167 168 169
## 43480.50 46873.43 42369.85 37708.60 43097.35 59289.89 61487.14 59911.90
## 170 171 172 173 174 175 176 177
## 56758.95 55168.96 57853.36 56884.02 49227.98 52032.50 55749.95 55785.89
## 178 179 180 181 182 183 184 185
## 62866.77 53432.95 50205.75 41076.25 32672.50 36157.16 46192.67 45652.34
## 186 187 188 189 190 191 192 193
## 51565.06 57271.38 67981.83 73229.15 66966.26 67158.39 74181.13 69886.67
## 194 195 196 197 198 199 200 201
## 65439.61 54755.82 48826.12 50240.83 45865.17 38086.67 44468.19 42707.51
## 202 203 204 205 206 207 208 209
## 42347.91 42823.04 49571.34 58400.75 58032.85 59746.05 61390.70 65156.37
## 210 211 212 213 214 215 216 217
## 74559.20 66695.29 54963.81 49608.90 40665.83 37644.02 40736.73 30824.07
## 218 219 220 221 222 223 224 225
## 47683.36 54954.89 55850.72 78462.95 85907.52 87897.27 95437.53 86449.34
## 226 227 228 229 230 231 232 233
## 80488.15 80072.90 76745.42 75912.55 80617.69 81972.78 76445.69 71690.85
## 234 235 236 237 238 239 240 241
## 77306.44 64044.11 56134.21 48139.03 39807.06 43970.75 46109.00 39603.77
## 242 243 244 245 246 247 248 249
## 33325.88 43539.13 37824.75 41733.96 34049.91 32764.48 36395.16 39200.04
## 250 251 252 253 254 255 256 257
## 30091.24 35989.33 39765.16 44924.97 47624.98 47120.97 57352.58 74731.99
## 258 259 260 261 262 263 264 265
## 74548.29 67903.98 69375.84 65622.07 67058.48 60858.18 50205.01 46259.11
## 266 267 268 269 270 271 272 273
## 46467.11 42600.04 51344.91 47643.08 51761.90 49891.15 53931.13 54225.16
## 274 275 276 277 278 279 280 281
## 60202.08 57852.24 67460.10 61425.78 61634.40 59994.58 65698.80 59471.49
## 282 283 284 285 286 287 288 289
## 56058.47 45681.10 44088.63 61204.89 66697.77 67104.47 64539.44 63632.95
## 290 291 292 293 294 295 296 297
## 67606.99 71490.91 52615.34 42783.99 36835.33 47087.26 50307.27 49427.18
## 298 299 300 301 302 303 304 305
## 73460.70 79366.10 80029.27 84610.69 82821.38 77885.07 81328.58 56396.25
## 306 307 308 309 310 311 312 313
## 52682.52 52364.45 46196.38 43424.60 47003.71 39606.00 38336.69 32934.80
## 314 315 316 317 318 319 320 321
## 36693.89 35880.74 39686.45 37684.43 63297.40 61153.08 62765.75 70710.11
## 322 323 324 325 326 327 328 329
## 73081.39 98391.78 96780.35 72772.99 71578.92 69947.28 61953.09 59094.66
## 330 331 332 333 334 335 336 337
## 29244.99 32907.77 33344.35 35648.70 34993.96 40722.23 41790.61 37070.10
## 338 339 340 341 342 343 344 345
## 36289.80 36415.61 31766.01 37724.72 38383.67 38640.38 39510.31 41284.24
## 346 347 348 349 350 351 352 353
## 43094.01 42847.33 35839.34 26470.47 31779.28 30647.93 30240.73 27873.16
## 354 355 356 357 358 359 360 361
## 32520.66 36249.76 40683.43 38884.57 40136.41 42260.52 49606.17 50153.07
## 362 363 364 365 366 367 368 369
## 50353.38 52800.03 50300.33 49748.60 42443.11 39658.68 35861.28 33654.86
## 370 371 372 373 374 375 376 377
## 29739.08 36978.13 39249.75 47082.42 41094.47 40545.34 39092.45 38628.36
## 378 379 380 381 382 383 384 385
## 29557.23 34124.53 27222.77 35387.02 45650.85 49192.41 47477.35 49455.69
## 386 387 388 389 390 391 392 393
## 55634.17 65056.09 58312.87 52818.13 52548.65 59878.81 60398.68 69009.30
## 394 395 396 397 398 399 400 401
## 58187.92 59744.31 59307.37 58794.68 57291.71 56061.57 42908.82 51437.01
## 402 403 404 405 406 407 408 409
## 50444.12 49417.27 55775.73 48368.10 47683.86 46021.73 41727.88 40566.41
## 410 411 412 413 414 415 416 417
## 38643.23 32776.88 40597.03 43503.81 38211.74 33332.82 48105.06 51924.16
## 418 419 420 421 422 423 424 425
## 55827.17 48346.65 44694.53 43379.35 46941.86 35438.34 35169.36 29466.50
## 426 427 428 429 430 431 432 433
## 35019.00 43290.97 50137.08 46924.13 44012.40 40958.12 40847.05 37348.38
## 434 435 436 437 438 439 440 441
## 33512.56 30765.06 32330.63 34167.67 32185.60 37019.28 43183.38 39957.42
## 442 443 444 445 446 447 448 449
## 39665.38 42651.73 40551.91 44514.79 39793.42 30885.55 29730.77 41011.91
## 450 451 452 453 454 455 456 457
## 40695.58 46309.06 41974.31 42321.63 43931.33 47624.73 37566.55 42383.49
## 458 459 460 461 462 463 464 465
## 37837.27 45355.84 48852.40 51455.48 48206.83 50532.00 50732.19 52467.58
## 466 467 468 469 470 471 472 473
## 51968.53 54892.66 52238.76 57256.26 50561.13 48187.99 46783.57 43430.42
## 474 475 476 477 478 479 480 481
## 47161.51 54575.09 49039.20 50731.08 45554.66 43944.47 46758.53 36243.31
## 482 483 484 485 486 487 488 489
## 29847.79 31704.65 34383.35 35863.39 36822.81 30506.00 42952.70 49568.49
## 490 491 492 493 494 495 496 497
## 56353.24 51099.97 55900.80 63483.58 67269.58 53617.52 44258.20 42296.35
## 498 499 500 501 502 503 504 505
## 42617.39 43403.28 37926.76 40309.70 45575.86 51219.59 51924.53 52034.35
## 506 507 508 509 510 511 512 513
## 45778.28 47109.32 43393.48 46130.56 45798.49 39556.17 40679.71 39860.36
## 514 515 516 517 518 519 520 521
## 40958.61 43581.03 36468.91 31779.77 55510.09 63616.34 67244.91 60668.16
## 522 523 524 525 526 527 528 529
## 61998.71 75493.71 82423.85 57541.24 52445.52 49161.92 53511.67 53021.30
## 530 531 532 533 534 535 536 537
## 43270.40 48228.90 60804.88 55362.33 58737.29 62698.44 59770.72 54834.65
## 538 539 540 541 542 543 544 545
## 48340.58 47005.45 54889.56 54641.65 47251.38 49460.15 49287.98 49977.18
## 546 547 548 549 550 551 552 553
## 40688.63 32578.92 36891.73 44954.09 44752.42 46446.90 40496.75 49449.49
## 554 555 556 557 558 559 560 561
## 50597.08 40444.56 49921.77 57732.01 57101.19 60674.85 56470.38 68152.27
## 562 563 564 565 566 567 568 569
## 84729.32 74886.57 63526.59 67916.26 66053.19 67231.89 58858.27 42954.43
## 570 571 572 573 574 575 576 577
## 49916.19 55781.80 56956.66 59018.30 59661.01 56806.67 68971.37 58415.87
## 578 579 580 581 582
## 52180.87 59731.55 61225.47 54365.73 60590.31
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8382
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 6.567018 0.556034 3.400656
## t2* 1749.167083 23.901807 223.157175
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.404235 6.653381 13.3661
## 2 lag_depvar 1425.986103 1762.733646 2162.5704Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon May 22 00:52:46 2023
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 0.0000 | 5.410333 | 5.629750 | 6.359175 |
| Comida | 370.2575 | 310.278417 | 314.087500 | 343.358350 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 31.5895 | 47.072333 | 38.297667 | 32.318925 |
| Enceres | 33.2250 | 20.086417 | 17.443792 | 25.492375 |
| Farmacia | 4.9950 | 1.831667 | 7.913875 | 9.458850 |
| Gas/Bencina | 44.0600 | 44.325000 | 28.954333 | 26.956100 |
| Diosi | 12.1450 | 31.180667 | 41.934250 | 37.511450 |
| donaciones/regalos | 0.0000 | 0.000000 | 7.170083 | 6.867975 |
| Electrodomésticos/ Mantención casa | 0.0000 | 3.944000 | 30.269500 | 20.736700 |
| VTR | 10.9950 | 25.156667 | 22.121792 | 20.106700 |
| Netflix | 5.6450 | 7.151583 | 7.090167 | 7.292775 |
| Otros | 0.0000 | 3.151083 | 1.575542 | 0.945325 |
| Total | 512.9120 | 499.588167 | 522.488250 | 537.404700 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1986, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-06-09 00:04:58 sería de: 36.396 pesos// Percentil 95% más alto proyectado: 39.596,03
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36066.76 | 36065.69 |
| Lo.80 | 36071.90 | 36069.74 |
| Point.Forecast | 36395.64 | 37104.26 |
| Hi.80 | 38191.54 | 41922.45 |
| Hi.95 | 39177.83 | 44473.05 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3254 1006.1984
## s.e. 0.1382 33.3749
##
## sigma^2 = 27182: log likelihood = -331.76
## AIC=669.53 AICc=670.04 BIC=675.32paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2965 656.0032 11.4970
## s.e. 0.1413 367.8739 12.0162
##
## sigma^2 = 27292: log likelihood = -331.33
## AIC=670.66 AICc=671.53 BIC=678.39forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 767.4879 | 664.4556 | 699.4717 |
| Lo.80 | 884.8408 | 782.7447 | 812.0492 |
| Point.Forecast | 1106.5258 | 1006.1981 | 1024.7130 |
| Hi.80 | 1328.2108 | 1229.6515 | 1285.1505 |
| Hi.95 | 1445.5638 | 1347.9405 | 1423.0177 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.28 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.1 xts_0.13.1
## [13] forecast_8.21 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.2 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
## [28] sjPlot_2.8.14 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.1 httr_1.4.6
## [34] readxl_1.4.2 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 DataExplorer_0.8.2 data.table_1.14.8
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.8
## [43] readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 igraph_1.4.2 lazyeval_0.2.2
## [7] splines_4.1.2 crosstalk_1.2.0 digest_0.6.31
## [10] htmltools_0.5.5 fansi_1.0.4 ggfortify_0.4.16
## [13] magrittr_2.0.3 tzdb_0.4.0 modelr_0.1.11
## [16] vroom_1.6.3 timechange_0.2.0 anytime_0.3.9
## [19] tseries_0.10-54 colorspace_2.1-0 xfun_0.39
## [22] crayon_1.5.2 jsonlite_1.8.4 lme4_1.1-33
## [25] glue_1.6.2 r2d3_0.2.6 gtable_0.3.3
## [28] emmeans_1.8.6 sjstats_0.18.2 sjmisc_2.8.9
## [31] car_3.1-2 quantmod_0.4.22 abind_1.4-5
## [34] mvtnorm_1.1-3 DBI_1.1.3 ggeffects_1.2.2
## [37] Rcpp_1.0.10 viridisLite_0.4.2 xtable_1.8-4
## [40] performance_0.10.3 bit_4.0.5 htmlwidgets_1.6.2
## [43] timeSeries_4021.105 gplots_3.1.3 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.5 dbplyr_2.3.2
## [52] janitor_2.2.0 utf8_1.2.3 tidyselect_1.2.0
## [55] labeling_0.4.2 rlang_1.1.0 munsell_0.5.0
## [58] cellranger_1.1.0 tools_4.1.2 cachem_1.0.7
## [61] cli_3.6.1 generics_0.1.3 sjlabelled_1.2.0
## [64] broom_1.0.4 evaluate_0.20 fastmap_1.1.1
## [67] yaml_2.3.7 knitr_1.42 bit64_4.0.5
## [70] caTools_1.18.2 forge_0.2.0 nlme_3.1-153
## [73] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [76] compiler_4.1.2 rstudioapi_0.14 curl_5.0.0
## [79] bslib_0.4.2 highr_0.10 fBasics_4022.94
## [82] Matrix_1.5-4.1 its.analysis_1.6.0 nloptr_2.0.3
## [85] urca_1.3-3 vctrs_0.6.1 pillar_1.9.0
## [88] lifecycle_1.0.3 networkD3_0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 estimability_1.4.1 bitops_1.0-7
## [94] insight_0.19.1 R6_2.5.1 KernSmooth_2.23-20
## [97] janeaustenr_1.0.0 codetools_0.2-18 gtools_3.9.4
## [100] boot_1.3-28 MASS_7.3-54 assertthat_0.2.1
## [103] rprojroot_2.0.3 withr_2.5.0 fracdiff_1.5-2
## [106] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4022.108 minqa_1.2.5
## [112] snakecase_0.11.0 rmarkdown_2.21 carData_3.0-5
## [115] TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))